a b s t r a c tThis paper deals with the fault diagnosis of the face milling tool based on machine learning approach using histogram features and K-star algorithm technique. Vibration signals of the milling tool under healthy and different fault conditions are acquired during machining of steel alloy 42CrMo4. Histogram features are extracted from the acquired signals. The decision tree is used to select the salient features out of all the extracted features and these selected features are used as an input to the classifier. K-star algorithm is used as a classifier and the output of the model is utilised to study and classify the different conditions of the face milling tool. Based on the experimental results, K-star algorithm is provided a better classification accuracy in the range from 94% to 96% with histogram features and is acceptable for fault diagnosis.

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Successful automation of a machining process system requires an effective and efficient tool condition monitoring system to ensure high productivity, products of desired dimensions, and long machine tool life. As such the component's processing quality and increased system reliability will be guaranteed. This paper presents a classification of healthy and faulty conditions of the face milling tool by using the Naïve Bayes technique. A set of descriptive statistical parameters is extracted from the vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features are fed to the Naïve Bayes algorithm. The output of the algorithm is used to study and classify the milling tool condition and it is found that the Naïve Bayes model is able to give 96.9% classification accuracy. Also the performances of the different classifiers are compared. Based on the results obtained, the Naïve Bayes technique can be recommended for online monitoring and fault diagnosis of the face milling tool.

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